A multiplicity of applications in medical image processing require image data to be registered as a necessary algorithmic method step. In the process, a movement of the patient due to illness, pain, breathing or another natural movement can lead to artifacts in the displayed image; in particular, a movement of the patient leads to the recorded organs no longer being imaged on the same pixels or voxels, that is to say they are no longer at the same location in an image. Such movements constitute a major problem when calculating perfusion measurements, in which the through-flow of the contrast agent per unit time must be traced as precisely as possible for each voxel in an image data record.
In order to establish a reliable perfusion measurement, a technique for motion detection and correction in time sequences has to be established. A simple known possibility is visual estimation and the removal of images which reproduce times at which a movement occurs. Subsequently only the remaining images are used to calculate the perfusion parameters.
Until now, the prior art has only published little work regarding highly developed and automated methods. In the document WO 2006/086 845 A1, Yang et al. describe a method for examining movements in perfusion time sequences. In this case, the average intensity value of a selected region of interest is plotted against time. The resultant curve is smoothed, and the differences between the plotted data points and the curve are measured. Local minima and maxima of the difference curve are detected in this manner and the interpolation is used to minimize motion artifacts. The big disadvantage of this method is that the average signal is related to motion detection rather than the congruence of voxels. Furthermore, the movement is compensated for by interpolation rather than registration, as a result of which the method is not accurate enough for calculating perfusion parameters.
The document by M. Hemmendorff, M. Andersson, H. Knutsson, “Phase-based Image Motion Estimation and Registration”, ICASSP '99, Phoenix, Ariz., USA, March 1999, is also known. Here, movement compensation by quadratic filters combined with parameter models is presented for movement compensation relating to angiography data. Although this approach is very interesting, the complexity of this model seems to preclude it from repeated application relating to a perfusion measurement due to a lack of efficiency.
A method for recording MR relaxation time series with a low signal was disclosed in the document U.S. Pat. No. 6,687,528 by Gupta et al. High-contrast anatomical images are reconstructed in this method in addition to relaxation time images. The registered data of the continuous high-contrast images are subsequently transferred to the noisy relaxation time images.
In the patent specification U.S. Pat. No. 6,718,055, Suri et al. use a mutual information technique, that is to say transinformation, based on estimating Parzen windows to calculate the temporal perfusion images. However, the inventors do not subsequently analyze the time series to optimize the recording process.
Furthermore, reference is made to the document by Zhuang et al., “Adaptive key frame extraction using unsupervised clustering”, IEEE-Proceedings, Image Processing 1998 (ICIP 98), pp. 866-870. This document discloses subdividing an image series into intervals using a color-related threshold algorithm and selecting appropriate key frames or interval reference comparison image data records. However, no transformation function between the reference image comparison data records, and hence between the intervals, is calculated to correct the movement; rather the key frames are used for video abstraction and summarization.